An inner product space approach to image coding by contractive transformations

Oien, G.E.; Lepsøy, Skjalg; Ramstad, T.A.

doi:10.1109/icassp.1991.150977

Cited by 45 publications

(16 citation statements)

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“…It is worthy to note that the bit-rate is also decreased, as the sizes of the codebooks are reduced. On the other hand, the conventional fractal coders [2], [3], [10] cannot decrease the bit-rate, even if they reduce the size of the codebook by employing the classification or clustering scheme, since the decoder cannot be informed of the exact codebook generated in the encoder. Fig.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…And O is the orthogonalization operator proposed by Øien [3]. The contraction mapping for the source image is composed of these blockwise mappings for each Ri, and the decoder applies the contraction mapping to an arbitrary image iteratively to reconstruct the attractor image.…”

Section: Fractal Vector Quantization (Fvq)mentioning

confidence: 99%

“…More specifically, even if the codebook of size N is generated from the domain pool of size M (N < M) by some classification or selection algorithm, the codeword should be indexed by log 2 M b [2], [3], [10], or the N addresses of the selected codewords should be transmitted to the decoder as side information [6], [7]. Therefore, in this correspondence, we propose the FVQ, which can index the codeword by log 2 N b without any side information, as well as exploit the self-similarity in real images.…”

Section: Fractal Vector Quantization (Fvq)mentioning

confidence: 99%

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A fractal vector quantizer for image coding

Kim

Lee

1998

IEEE Trans. on Image Process.

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We investigate the relation between VQ (vector quantization) and fractal image coding techniques, and propose a novel algorithm for still image coding, based on fractal vector quantization (FVQ). In FVQ, the source image is approximated coarsely by fixed basis blocks, and the codebook is self-trained from the coarsely approximated image, rather than from an outside training set or the source image itself. Therefore, FVQ is capable of eliminating the redundancy in the codebook without any side information, in addition to exploiting the self-similarity in real images effectively. The computer simulation results demonstrate that the proposed algorithm provides better peak signal-to-noise ratio (PSNR) performance than most other fractal-based coders.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Fractal Vector Quantization (Fvq)mentioning

confidence: 99%

Section: Fractal Vector Quantization (Fvq)mentioning

confidence: 99%

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A fractal vector quantizer for image coding

Kim

Lee

1998

IEEE Trans. on Image Process.

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“…[6] introduces So, to a method of searching for an optimal pair of a and b which is accepted now widely. In the following a shortcoming of it that a and b is correlated is pointed out.…”

Section: Shortcomings Of Common Fractal Image Coding Methods and mentioning

confidence: 99%

A new fractal image coding method

Yin

Yang

1998

J. of Electron.(China)

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“…In fact, experimental results have shown that blocks are likely to be badly estimated with a single block, even if photometric compensation is performed. Following previous advances in fractal coding [12,25], the idea is then to combine several blocks BQ i in the codebook Q to obtain a better replacement block:…”

Section: Blocks Combinationmentioning

confidence: 99%

Exploiting self-similarities to defeat digital watermarking systems

Doerr

Dugelay

Grangé

2004

Proceedings of the 2004 Workshop on Multimedia and Security

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Unauthorized digital copying is a major concern for multimedia content providers. Since copyright owners lose control over content distribution as soon as data is decrypted or unscrambled, digital watermarking has been introduced as a complementary protection technology. In an effort to anticipate hostile behaviors of adversaries, the research community is constantly introducing novel attacks to benchmark watermarking systems. In this paper, a generic block replacement attack will be presented. The underlying assumption is that multimedia content is highly repetitive. It should consequently be possible to exploit the self-similarities of the signal to replace each signal block with another perceptually similar one. Alternative methods to compute such a valid replacement block will be surveyed in this paper. Then, experimental results on still images will be presented to demonstrate the efficiency of the presented attack in comparison with other reference image processing operations. Finally, a discussion will be conducted to exhibit the properties that a watermark should have to resist to this attack.

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An inner product space approach to image coding by contractive transformations

Cited by 45 publications

References 1 publication

A fractal vector quantizer for image coding

A fractal vector quantizer for image coding

A new fractal image coding method

Exploiting self-similarities to defeat digital watermarking systems

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